docs: add openrank overview (#94)

Signed-off-by: frank-zsy <[email protected]>

docs/user_docs/metrics/openrank.md

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+# OpenRank 算法
+
+OpenRank 算法是由 X-lab 实验室提出的一种开源评价系列算法，该系列算法为工信部标准院[《信息技术 开源治理》](https://www.ttbz.org.cn/Home/Standard?searchType=3&key=%E5%BC%80%E6%BA%90%E6%B2%BB%E7%90%86)系列标准中项目评估与开发者贡献度评估中网络评估模型的标准开源实现。
+
+## PageRank 算法
+
+<div style={{'text-align':'center'}}>
+  <img src="/img/pagerank.svg" alt="PageRank" width="300px" />
+</div>
+
+PageRank 算法在 1998 年由 Google 的创始人拉里·佩奇和谢尔盖·布林提出一种网络中心性评估算法。其优势在于网页的质量的评估可以不需要关注网页中的具体内容，而是通过分析网页之间的引用关系并配合 PageRank 网络算法即可得到。而 PageRank 的基本价值主张是高质量的网页会被更多的网页所引用，而高质量的网页也会倾向于引用其他高质量的网页，即网页之间的引用关系代表每个网页的开发者或拥有者对其引用网页的价值认可。PageRank 的提出颠覆了之前的搜索引擎基于搜索关键词相关性与网页文本内容分析的排序方法，在极大降低分析成本的同时有效提升了搜索结果的质量。
+
+## OpenRank 算法
+
+OpenRank 受启发于 PageRank，但在其基础上进行了优化和扩展，使得该网络算法支持为节点引入初值作为固有属性，并支持将节点的中心性推广到高维空间。
+
+节点初值的引入使得在实际应用场景中，节点的中心性可以继承其历史数据，从而具有较好的时序连续性，同时
+
+OpenRank 在开源中的应用由两个子算法组成，分别为[全域 OpenRank 影响力算法](global_openrank)和[社区 OpenRank 贡献度算法](community_openrank)。
+
+## OpenRank 的价值主张
+
+- OpenRank 旨在挖掘社会化协作网络中的价值流动，在深入挖掘协作关系的同时也支持通过科研结果不断优化算法细节，因此具有更好的适用性。
+- OpenRank 的提出旨在解决统计型指标中广泛存在的观察者效应，如开源项目关注 Star 数量、贡献者数量等指标带来的行为异化和对社区的伤害。
+- OpenRank 算法将社区制度设计以算法形式进行固化，将度量与行为影响深度一体化，通过算法度量引导开发者进行健康的长期协作。

i18n/en/docusaurus-plugin-content-docs/current/user_docs/metrics/openrank.md

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+# OpenRank Algorithm
+
+The OpenRank algorithm is an open source evaluation series algorithm proposed by the X-lab laboratory. This series of algorithms serves as the standard open source implementation for project evaluation and developer contribution assessment in the network evaluation model of the "Information Technology Open Source Governance" series standards by the Ministry of Industry and Information Technology Standardization Institute [《Information Technology Open Source Governance》](https://www.ttbz.org.cn/Home/Standard?searchType=3&key=%E5%BC%80%E6%BA%90%E6%B2%BB%E7%90%86).
+
+## PageRank Algorithm
+
+<div style={{'text-align':'center'}}>
+  <img src="/img/pagerank.svg" alt="PageRank" width="300px" />
+</div>
+
+The PageRank algorithm was proposed in 1998 by Google founders Larry Page and Sergey Brin as a network centrality assessment algorithm. Its advantage is that the quality of web pages can be evaluated without focusing on the specific content of the pages, but rather by analyzing the reference relationships between the pages combined with the PageRank network algorithm. The fundamental proposition of PageRank is that high-quality web pages are referenced by more pages, and high-quality pages tend to reference other high-quality pages, meaning the reference relationships between pages represent the value recognition of each page's developer or owner towards the referenced pages. The introduction of PageRank revolutionized the previous search engine methodologies, which were based on search keyword relevance and web page content analysis, significantly reducing analysis costs while effectively enhancing the quality of search results.
+
+## OpenRank Algorithm
+
+Inspired by PageRank, OpenRank has been optimized and expanded upon, allowing the network algorithm to introduce initial values as inherent attributes for nodes and supporting the extension of nodes' centrality to higher-dimensional spaces.
+
+The introduction of initial values for nodes allows, in practical applications, for the centrality of nodes to inherit historical data, thus maintaining good temporal continuity, while
+
+OpenRank in open source is composed of two sub-algorithms: [Global OpenRank Influence Algorithm](global_openrank) and [Community OpenRank Contribution Algorithm](community_openrank).
+
+## Value Proposition of OpenRank
+
+- OpenRank aims to explore the flow of value in social collaborative networks, delving deep into collaborative relationships while also supporting continuous optimization of algorithm details through scientific results, thereby offering better applicability.
+- OpenRank was introduced to address the widespread observer effect in statistical indicators, such as the alienation behavior and community damage caused by metrics like the number of stars and contributors in open source projects.
+- The OpenRank algorithm solidifies community institutional design in algorithmic form, integrating measurement with behavioral impact, guiding developers towards healthy, long-term collaboration through algorithmic metrics.

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